Issue 11, 2022

Rapid identification of human muscle disease with fibre optic Raman spectroscopy


The diagnosis of muscle disorders (“myopathies”) can be challenging and new biomarkers of disease are required to enhance clinical practice and research. Despite advances in areas such as imaging and genomic medicine, muscle biopsy remains an important but time-consuming investigation. Raman spectroscopy is a vibrational spectroscopy application that could provide a rapid analysis of muscle tissue, as it requires no sample preparation and is simple to perform. Here, we investigated the feasibility of using a miniaturised, portable fibre optic Raman system for the rapid identification of muscle disease. Samples were assessed from 27 patients with a final clinico-pathological diagnosis of a myopathy and 17 patients in whom investigations and clinical follow-up excluded myopathy. Multivariate classification techniques achieved accuracies ranging between 71–77%. To explore the potential of Raman spectroscopy to identify different myopathies, patients were subdivided into mitochondrial and non-mitochondrial myopathy groups. Classification accuracies were between 74–89%. Observed spectral changes were related to changes in protein structure. These data indicate fibre optic Raman spectroscopy is a promising technique for the rapid identification of muscle disease that could provide real time diagnostic information. The application of fibre optic Raman technology raises the prospect of in vivo bedside testing for muscle diseases which would significantly streamline the diagnostic pathway of these disorders.

Graphical abstract: Rapid identification of human muscle disease with fibre optic Raman spectroscopy

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Article information

Article type
25 Oct 2021
24 Mar 2022
First published
21 Apr 2022
This article is Open Access
Creative Commons BY license

Analyst, 2022,147, 2533-2540

Rapid identification of human muscle disease with fibre optic Raman spectroscopy

J. J. P. Alix, M. Plesia, G. R. Lloyd, A. P. Dudgeon, C. A. Kendall, C. Hewamadduma, M. Hadjivassiliou, C. J. McDermott, G. S. Gorman, R. W. Taylor, P. J. Shaw and J. C. C. Day, Analyst, 2022, 147, 2533 DOI: 10.1039/D1AN01932E

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